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The trickiest part of hunting for new elementary particles is sifting through the massive amounts of data to find telltale patterns, or "signatures," for those particles—or, ideally, weird patterns that don't fit any known particle, an indication of new physics beyond the so-called Standard Model. MIT physicists have developed an analytical method to essentially automate these kinds of searches. The method is based on how similar pairs of collision events are to one another and how hundreds of thousands of such events are related to each other.

The result is an intricate geometric map, dubbed a "collision network," that is akin to mapping complex social networks. The MIT team described its novel approach in a new paper in Physical Review Letters: "Maps of social networks are based on the degree of connectivity between people, and for example, how many neighbors you need before you get from one friend to another," co-author Jesse Thaler said. "It's the same idea here."

The Large Hadron Collider (LHC) produces billions of proton [corrected] collisions per minute. Physicists identify exactly which particles are produced in high-energy collisions by the electronic signatures the particles leave behind, known as nuclear decay patterns. Quarks, for instance, only exist for fractions of a second before they decay into other secondary particles. Since each quark has many different ways of decaying, there are several possible signatures, and each must be carefully examined to determine which particles were present at the time of the collision.

Detectors like the Compact Muon Solenoid (CMS) collaboration filter out signals using so-called "triggers"—set off when an event indicates a specific particle of interest, or a potentially new particle, out of the tens of thousands of signals created every millionth of a second inside the accelerator.

“Maps of social networks are based on the degree of connectivity between people. It’s the same idea here.”

Here's an example: if a proton-antiproton collision produces a top quark and an antitop particle, these will instantly decay into two weak force (W) bosons and two bottom quarks. One of the "offspring" bosons turns into a muon and a neutrino, while the other decays into up and down quarks. The two bottom quarks decay into two jets of particles, as do the up and down quarks. So the signature of the collision is a muon, a neutrino, and four jets.

"Jets" appear because quarks can't exist in isolation; they must be bound inside hadrons. Whenever a quark is produced in a collision, it goes flying out of its host hadron, surrounded by a spray of hadrons, all traveling pretty much in the same direction. Studying the jet spray enables physicists to determine what kind of quark produced it.

Back in 2017, Thaler and his colleagues applied some of their novel analytical methods to a huge dataset from the CMS detector. The dataset consisted of some 29 terabytes of data involving about 300 million proton collisions within the LHC and had been released onto the CERN Open Data Portal. The idea was to demonstrate the usefulness of such methods to make sense out of that mountain of information.

This latest work builds on that. It is especially well-suited for hunting for new physics that falls outside existing theories—in other words, cases where physicists wouldn't know ahead of time what signatures they're looking for.

Enlarge/ Signature of a possible top-quark-pair candidate. Tracks shown are from the decays of two top quarks produced in a collision.

Fermilab

The basic idea is to compare many different events to each other, rather than analyzing each one individually. The spray of particles produced in a collision is modeled as a point cloud, like those used in computer vision for representing objects. This lets physicists clearly identify typical behaviors and more easily pick out outliers lurking at the fringes of the collision network.

"What we're trying to do is to be agnostic about what we think is new physics or not," said co-author Eric Metodiev. "We want to let the data speak for itself."

Key to this novel analytical method is an algorithm that calculates how much energy (or "work" in physics parlance) is required for one cloud in a pair to transform into another. This concept is dubbed the "earth mover's distance," or EMD. A pair of point clouds would be deemed farther apart if it takes a lot of energy to rearrange one into the other.

"You can imagine deposits of energy as being dirt, and you're the earth mover who has to move that dirt from one place to another," said Prof. Thaler. "The amount of sweat that you expend getting from one configuration to another is the notion of distance that we're calculating."

Using public data from the LHC, the MIT team constructed a social network of 100,000 pairs of collision events, assigning a number to each pair based on the "distance," or similarity, between them. Thaler would like to further test the team's technique on known historical data, such as rediscovering the top quark (first observed in 1995).

"If we could rediscover the top quark in this archival data, with this technique that doesn't need to know what new physics it is looking for, it would be very exciting and could give us confidence in applying this to current datasets, to find more exotic objects," said Thaler.

"It will be interesting to see where the ideas and techniques presented in this short and thought-provoking paper will bring us," wrote Michael Schmitt at APS Physics (Prof. Schmitt was not involved in the new paper). "The new EMD-based metric may well lead to better event classification techniques that enable experimenters to discover new physics beyond the Standard Model."

FFS please change this garbage title. The paper has nothing to do with social networks and they've been mentioned once as helping example to dumb it down for the blog post. So implying that they said something like "MIT physicists: Social networks could hold the key to finding new particles" is just bad journalism. That's not on the level that anyone here expects from Ars.

As for the paper itself - really interesting use of ML in particle physics.

Edit: Apologies for ranting, but I honestly think it's well deserved here.

FFS please change this garbage title. The paper has nothing to do with social networks and they've been mentioned once as helping example to dumb it down for the blog post. So implying that they said something like "MIT physicists: Social networks could hold the key to finding new particles" is just bad journalism. That's not on the level that anyone here expects from Ars.

As for the paper itself - really interesting use of ML in particle physics.

Edit: Apologies for ranting, but I honestly think it's well deserved here.

And there was me thinking they'd found a U-238 nucleus with a Facebook Like button on it.

It would have been better to have written that the same kind of techniques used in data mining social networks are being applied to particle physics. Which is unsurprising.Physics was relatively slow to adopt the statistical methods already in use for sociology and genetics because physicists liked to think that theirs was an "exact" science and didn't suffer from statistical variation. An example is Eddington's "proof" of Einstein - later analysis showed that the photographs did not reliably support his argument. In the early days of quantum mechanics there was still a rearguard reaction against the statistical nature of quantum phenomena even though things like gas pressure are artefacts of processes that respond to statistical analysis.Nowadays when result significance is measured in standard deviations we're past that, but there still seems (as with the parent post) an unwillingness to accept that the same techniques can apply both to particles and people. Whether they should be allowed to be applied to people for any reason other than disease prevention and treatment is, of course, another matter.

FFS please change this garbage title. The paper has nothing to do with social networks and they've been mentioned once as helping example to dumb it down for the blog post. So implying that they said something like "MIT physicists: Social networks could hold the key to finding new particles" is just bad journalism. That's not on the level that anyone here expects from Ars.

As for the paper itself - really interesting use of ML in particle physics.

Edit: Apologies for ranting, but I honestly think it's well deserved here.

I was super disappointed that it was just a bad metaphor. I thought there may have been an actual use for social media networks.

FFS please change this garbage title. The paper has nothing to do with social networks and they've been mentioned once as helping example to dumb it down for the blog post. So implying that they said something like "MIT physicists: Social networks could hold the key to finding new particles" is just bad journalism. That's not on the level that anyone here expects from Ars.

As for the paper itself - really interesting use of ML in particle physics.

Edit: Apologies for ranting, but I honestly think it's well deserved here.

I was super disappointed that it was just a bad metaphor. I thought there may have been an actual use for social media networks.

I figured it had something to do with distributed computing. Or at the very least, some bad particle physics jokes. "Two quarks walk into a bar..."

FML I've been reading too much about Donald Trump lately and read "collusion networks". I have nothing to add to the story other than it seems like a good idea for finding out unknowns with no hypothesis.

FFS please change this garbage title. The paper has nothing to do with social networks and they've been mentioned once as helping example to dumb it down for the blog post. So implying that they said something like "MIT physicists: Social networks could hold the key to finding new particles" is just bad journalism. That's not on the level that anyone here expects from Ars.

As for the paper itself - really interesting use of ML in particle physics.

Edit: Apologies for ranting, but I honestly think it's well deserved here.

And there was me thinking they'd found a U-238 nucleus with a Facebook Like button on it.

It would have been better to have written that the same kind of techniques used in data mining social networks are being applied to particle physics. Which is unsurprising.Physics was relatively slow to adopt the statistical methods already in use for sociology and genetics because physicists liked to think that theirs was an "exact" science and didn't suffer from statistical variation. An example is Eddington's "proof" of Einstein - later analysis showed that the photographs did not reliably support his argument. In the early days of quantum mechanics there was still a rearguard reaction against the statistical nature of quantum phenomena even though things like gas pressure are artefacts of processes that respond to statistical analysis.Nowadays when result significance is measured in standard deviations we're past that, but there still seems (as with the parent post) an unwillingness to accept that the same techniques can apply both to particles and people. Whether they should be allowed to be applied to people for any reason other than disease prevention and treatment is, of course, another matter.

Apparently the statistical concept behind Earth Mover's Distance originated in transportation theory in 1781, not in any kind of social network research, and more recently revived(?) for image matching, long before social networks. Sounds like social networks are just a recent application of the idea, but it may well be the physicists learned of it through social network application of the idea, so at least there might be that tenuous connection to hang that on.

The article wrongly describes the LHC as colliding protons and antiprotons. Nope, they only collide protons. At the energies LHC uses, the extra boost from annihilating protons and antiprotons is a drop in a bucket. They need high luminosity, and it's a lot easier to get a lot of protons than a mix of protons and antiprotons.

I know I can Google it (and did), but I would have preferred a brief explanation of what "rediscovering the top quark" is and the implication of that.

Also, I like Jennifer's writing quite a bit; it just seemed like this particular subject would've maybe been more up Chris Lee's alley.

Edit: accidentally a word

I took it as applying this new technique to a data set that we know at the time showed proof of a theorized-yet-unconfirmed with the goal of it identifying the collisions that in fact showed the proof. They're not rediscovering anything in reality, they're just testing their technique.

If they were successful in picking out the top quark collisions from that dataset it would help prove value in studying collisions their technique spits out in completely new datasets.

In my field of work where it's highly valuable to create cluster/groups (recursively) across n dimensions (imagine something to do with financial forensics - so we are looking for people related to bank accounts related to addresses themselves related to other people related to specific window of time etc.) Graph Theory and related tools are a godsend; classical analysis to relational DB are very slow and very cumbersome to do this work but packages in R make this a breeze.

Anyway any article focusing on social network analysis has to mention Graph Theory as it is the most relevant field and I can easily imagine how these kind of tools can cluster and help identify particles in a process not too dissimilar to my usage described above.

FFS please change this garbage title. The paper has nothing to do with social networks and they've been mentioned once as helping example to dumb it down for the blog post. So implying that they said something like "MIT physicists: Social networks could hold the key to finding new particles" is just bad journalism. That's not on the level that anyone here expects from Ars.

As for the paper itself - really interesting use of ML in particle physics.

Edit: Apologies for ranting, but I honestly think it's well deserved here.

And there was me thinking they'd found a U-238 nucleus with a Facebook Like button on it.

It would have been better to have written that the same kind of techniques used in data mining social networks are being applied to particle physics. Which is unsurprising.Physics was relatively slow to adopt the statistical methods already in use for sociology and genetics because physicists liked to think that theirs was an "exact" science and didn't suffer from statistical variation. An example is Eddington's "proof" of Einstein - later analysis showed that the photographs did not reliably support his argument. In the early days of quantum mechanics there was still a rearguard reaction against the statistical nature of quantum phenomena even though things like gas pressure are artefacts of processes that respond to statistical analysis.Nowadays when result significance is measured in standard deviations we're past that, but there still seems (as with the parent post) an unwillingness to accept that the same techniques can apply both to particles and people. Whether they should be allowed to be applied to people for any reason other than disease prevention and treatment is, of course, another matter.

Apparently the statistical concept behind Earth Mover's Distance originated in transportation theory in 1781, not in any kind of social network research, and more recently revived(?) for image matching, long before social networks. Sounds like social networks are just a recent application of the idea, but it may well be the physicists learned of it through social network application of the idea, so at least there might be that tenuous connection to hang that on.

The problem was expressed in the 1780s, but not as a statistical one (except in the weak sense of statistics as the numbers collected and analysed in order to facilitate the work of government.)I very carefully did not suggest that physicists had learned from social media, just that the same techniques might be in play, and that the article could have said that without being objectionable.

FFS please change this garbage title. The paper has nothing to do with social networks and they've been mentioned once as helping example to dumb it down for the blog post. So implying that they said something like "MIT physicists: Social networks could hold the key to finding new particles" is just bad journalism. That's not on the level that anyone here expects from Ars.

As for the paper itself - really interesting use of ML in particle physics.

Edit: Apologies for ranting, but I honestly think it's well deserved here.

I occasionally write for a popular science website (different physical science than what is covered here). These are the sort of titles you scribble up while thinking of your lead and then go back to and promptly change. Or, if it gets past the self assessment, it gets shot down in editorial discussions (hopefully) because it is misleading, only designed for clicks and has nothing to do with the science at hand.

What I don't get is that Jennifer wrote for Quanta and it is that similar style of writing that has always interested me with Ars articles and that Chris does pretty well. I really don't think the Ars audience needs things simplified so much as this title does and if it is trying to grab a different readership, is that the readership that Ars wants?

The article wrongly describes the LHC as colliding protons and antiprotons. Nope, they only collide protons. At the energies LHC uses, the extra boost from annihilating protons and antiprotons is a drop in a bucket. They need high luminosity, and it's a lot easier to get a lot of protons than a mix of protons and antiprotons.

Yeah, did a double take there also, good catch, maybe the author of the article should have run this one past "Hubby" (AKA Sean Carroll).

This whole issue of trying to find truly unknown new particles at LHC is a bit of a "chicken and egg" thing. It is extremely useful to have some general theoretical ideas of what you are looking for so you do not throw something interesting out in the first data processing cut that has to happen very soon, as the LHC (and collaborators) do not hold onto all of the "raw signal data".

sometime, these science people will find out why things/energies even exist....

It is all very simple, there is only one fundamental entity "thing" and it is energy, kinda like Einstein's "secrets of the old ONE", at least that is what I have read somewhere. But why energy even exist I leave to the philosophers. / just kidding

FFS please change this garbage title. The paper has nothing to do with social networks and they've been mentioned once as helping example to dumb it down for the blog post. So implying that they said something like "MIT physicists: Social networks could hold the key to finding new particles" is just bad journalism. That's not on the level that anyone here expects from Ars.

As for the paper itself - really interesting use of ML in particle physics.

Edit: Apologies for ranting, but I honestly think it's well deserved here.

I was super disappointed that it was just a bad metaphor. I thought there may have been an actual use for social media networks.

I figured it had something to do with distributed computing. Or at the very least, some bad particle physics jokes. "Two quarks walk into a bar..."

"If you want to hadronize here, bring your own quark, dammit. Otherwise I really have to see one of you leaving, and I mean that literally. We don't meson with deliverables here."

I think some of the people criticizing the "social networks" analogy are thinking of "social media networks". A "social network" doesn't necessarily refer to social media, it just refers to mapping out the connections between people. Social media allows those networks to be put together fairly easily on a large scale, but you can still have "social networks" based on entirely real-world connections between people, and analyze them using graph theory concepts.

The article wrongly describes the LHC as colliding protons and antiprotons. Nope, they only collide protons. At the energies LHC uses, the extra boost from annihilating protons and antiprotons is a drop in a bucket. They need high luminosity, and it's a lot easier to get a lot of protons than a mix of protons and antiprotons.

This whole issue of trying to find truly unknown new particles at LHC is a bit of a "chicken and egg" thing. It is extremely useful to have some general theoretical ideas of what you are looking for so you do not throw something interesting out in the first data processing cut that has to happen very soon, as the LHC (and collaborators) do not hold onto all of the "raw signal data".

This is a very good point. Tons of data is thrown away already in the detector as we simply can't handle the stream of data being produced. If those filters are "wrong" then later analysis will not really repair the damage done.

Still it can be useful as analytical tool and you never know, there might be some hints on the edges that can be found this way and lead to changing the filter parameters in the detectors.

I think some of the people criticizing the "social networks" analogy are thinking of "social media networks". A "social network" doesn't necessarily refer to social media, it just refers to mapping out the connections between people. Social media allows those networks to be put together fairly easily on a large scale, but you can still have "social networks" based on entirely real-world connections between people, and analyze them using graph theory concepts.

No I think we're arguing over 20th century computer science methods being boiled down to 21st century social media.

If you want to explain Graph theory using social networks is one thing... and quite honestly social networks aren't so well defined that you can pull it up as a safe example. Twitter is a directed graph, Facebook is an undirected graph. Are energy states acyclic?

Thaler would like to further test the team's technique on known historical data, such as rediscovering the top quark (first observed in 1995).

I'd have thought that the people in charge at CERN would want to test this and forward the necessary data?

The Top quark was discovered at Fermilab. I think they mean that data.

Thanks, My bad...

Wrong place my my thought still stands that people would surely want to test it and therefore be willing to forward the data?

I'd think they'd be willing. The issue would be that 20+ years on, it may take some effort to track down the raw data. Then someone has to figure out how to read it off of whatever media it's on, and get it onto their network. Then they have to figure out the data format, to understand what they have. Maybe it's already easily accessible, but it's not a given.